outfields=info.keys()),
name='datasource')
datasource.inputs.template = "%s/%s"
datasource.inputs.base_directory = data_path
datasource.inputs.field_template = dict(pet_image='data/%s/%s_%s.nii.gz',
roi_image='data/%s/%s.nii.gz',
t1_image='data/%s/%s.nii.gz')
datasource.inputs.template_args = info
datasource.inputs.sort_filelist = True
datasink = pe.Node(interface=nio.DataSink(), name="datasink")
datasink.inputs.base_directory = output_dir
datasink.overwrite = True
petquant = create_pet_quantification_wf("example_petquant")
workflow = pe.Workflow(name='ex_petquant')
workflow.base_dir = output_dir
workflow.connect([(infosource, datasource, [('subject_id', 'subject_id')])])
workflow.connect([(infosource, petquant, [('subject_id',
'inputnode.subject_id')])])
workflow.connect([(datasource, petquant, [('pet_image', 'inputnode.pet')])])
workflow.connect([(datasource, petquant, [('roi_image', 'inputnode.rois')])])
workflow.connect([(datasource, petquant, [('t1_image', 'inputnode.t1')])])
workflow.connect([(petquant, datasink, [
("outputnode.out_files", "@subject_id.out_files"),
("outputnode.corrected_pet_to_t1", "@subject_id.corrected_pet_to_t1"),
("outputnode.pet_results_npz", "@subject_id.pet_results_npz"),
def create_dmn_pipeline_step1(name="dmn_step1",
scale_by_glycemia=True,
manual_seg_rois=False):
inputfields = [
"subjects_dir", "subject_id", "dwi", "bvecs", "bvals", "fdgpet",
"dose", "weight", "delay", "glycemie", "scan_time"
]
if manual_seg_rois:
inputfields.append("manual_seg_rois")
inputnode = pe.Node(interface=util.IdentityInterface(fields=inputfields),
name="inputnode")
outputnode = pe.Node(
interface=util.IdentityInterface(
fields=[ # Outputs from the DWI workflow
"single_fiber_mask",
"fa",
"rgb_fa",
"md",
"mode",
"t1",
"t1_brain",
"wm_mask",
"term_mask",
"aparc_aseg",
"tissue_class_files",
"gm_prob",
"wm_prob",
"csf_prob",
# Outputs from registration and labelling
"rois",
"rois_to_dwi",
"wmmask_to_dwi",
"termmask_to_dwi",
"dwi_to_t1_matrix",
"highres_t1_to_dwi_matrix",
# Outputs from the PET workflow after SUV calculation
"SUV_corrected_pet_to_t1",
"AIF_corrected_pet_to_t1",
"pet_results_npz",
"pet_results_mat",
"orig_pet_to_t1",
# T1 in DWI space for reference
"t1_to_dwi",
"single_fiber_mask_cortex_only",
]),
name="outputnode")
t1_to_dwi = pe.Node(interface=fsl.ApplyXfm(), name='t1_to_dwi')
termmask_to_dwi = t1_to_dwi.clone("termmask_to_dwi")
compute_cmr_glc_interface = util.Function(
input_names=[
"subject_id", "in_file", "dose", "weight", "delay", "glycemie",
"scan_time"
],
output_names=["out_file", "cax2", "mecalc", "denom"],
function=CMR_glucose)
compute_AIF_PET = pe.Node(interface=compute_cmr_glc_interface,
name='compute_AIF_PET')
compute_SUV_interface = util.Function(input_names=[
"subject_id", "in_file", "dose", "weight", "delay", "scan_time",
"isotope", 'height', "glycemie"
],
output_names=["out_file"],
function=calculate_SUV)
compute_SUV_norm_glycemia = pe.Node(interface=compute_SUV_interface,
name='compute_SUV_norm_glycemia')
scale_PVC_matrix_interface = util.Function(
input_names=[
"subject_id", "in_file", "dose", "weight", "delay", "scan_time",
"isotope", 'height', "glycemie", "scale_SUV_by_glycemia"
],
output_names=["out_npz", "out_matlab_mat"],
function=scale_PVC_matrix_fn)
scale_PVC_matrix = pe.Node(interface=scale_PVC_matrix_interface,
name='scale_PVC_matrix')
scale_PVC_matrix.inputs.scale_SUV_by_glycemia = scale_by_glycemia
single_fiber_mask_cortex_only = pe.Node(
interface=fsl.MultiImageMaths(), name='single_fiber_mask_cortex_only')
single_fiber_mask_cortex_only.inputs.op_string = "-mul %s"
dtiproc = damaged_brain_dti_processing("dtiproc")
reg_label = create_reg_and_label_wf("reg_label", manual_seg_rois)
petquant = create_pet_quantification_wf("petquant", segment_t1=False)
workflow = pe.Workflow(name=name)
workflow.base_output_dir = name
workflow.connect([(inputnode, dtiproc,
[("subjects_dir", "inputnode.subjects_dir"),
("subject_id", "inputnode.subject_id"),
("dwi", "inputnode.dwi"), ("bvecs", "inputnode.bvecs"),
("bvals", "inputnode.bvals")])])
workflow.connect([(inputnode, reg_label, [("subject_id",
"inputnode.subject_id")])])
if manual_seg_rois:
workflow.connect([(inputnode, reg_label, [
("manual_seg_rois", "inputnode.manual_seg_rois")
])])
workflow.connect([(dtiproc, reg_label, [
("outputnode.wm_mask", "inputnode.wm_mask"),
("outputnode.term_mask", "inputnode.termination_mask"),
("outputnode.fa", "inputnode.fa"),
("outputnode.aparc_aseg", "inputnode.aparc_aseg"),
])])
workflow.connect([(reg_label, t1_to_dwi, [("outputnode.t1_to_dwi_matrix",
"in_matrix_file")])])
workflow.connect([(dtiproc, t1_to_dwi, [("outputnode.t1", "in_file")])])
workflow.connect([(dtiproc, t1_to_dwi, [("outputnode.fa", "reference")])])
workflow.connect([(inputnode, t1_to_dwi, [
(('subject_id', add_subj_name_to_T1_dwi), 'out_file')
])])
workflow.connect([(reg_label, termmask_to_dwi,
[("outputnode.t1_to_dwi_matrix", "in_matrix_file")])])
workflow.connect([(dtiproc, termmask_to_dwi, [("outputnode.term_mask",
"in_file")])])
workflow.connect([(dtiproc, termmask_to_dwi, [("outputnode.fa",
"reference")])])
workflow.connect([(inputnode, single_fiber_mask_cortex_only, [
(('subject_id', add_subj_name_to_cortex_sfmask), 'out_file')
])])
workflow.connect([(termmask_to_dwi, single_fiber_mask_cortex_only,
[("out_file", "operand_files")])])
workflow.connect([(dtiproc, single_fiber_mask_cortex_only,
[("outputnode.single_fiber_mask", "in_file")])])
workflow.connect([(inputnode, compute_SUV_norm_glycemia, [
("subject_id", "subject_id"),
("dose", "dose"),
("weight", "weight"),
("delay", "delay"),
("scan_time", "scan_time"),
])])
if scale_by_glycemia == True:
workflow.connect([(inputnode, compute_SUV_norm_glycemia,
[("glycemie", "glycemie")])])
# This is for the arterial input function approximation for the FDG uptake
workflow.connect([(inputnode, compute_AIF_PET, [
("subject_id", "subject_id"),
("dose", "dose"),
("weight", "weight"),
("delay", "delay"),
("glycemie", "glycemie"),
("scan_time", "scan_time"),
])])
workflow.connect([(inputnode, scale_PVC_matrix, [
("subject_id", "subject_id"),
("dose", "dose"),
("weight", "weight"),
("delay", "delay"),
("glycemie", "glycemie"),
("scan_time", "scan_time"),
])])
workflow.connect([(dtiproc, petquant, [
(('outputnode.tissue_class_files', select_GM), 'inputnode.gm_mask')
])])
workflow.connect([(dtiproc, petquant, [
("outputnode.t1", "inputnode.t1"),
("outputnode.wm_prob", "inputnode.wm_prob"),
("outputnode.gm_prob", "inputnode.gm_prob"),
("outputnode.csf_prob", "inputnode.csf_prob"),
])])
workflow.connect([(inputnode, petquant, [("fdgpet", "inputnode.pet")])])
workflow.connect([(inputnode, petquant, [("subject_id",
"inputnode.subject_id")])])
if manual_seg_rois:
workflow.connect([(inputnode, petquant, [("manual_seg_rois",
"inputnode.rois")])])
else:
workflow.connect([(reg_label, petquant, [("outputnode.rois",
"inputnode.rois")])])
workflow.connect([(petquant, compute_AIF_PET,
[("outputnode.corrected_pet_to_t1", "in_file")])])
workflow.connect([(petquant, compute_SUV_norm_glycemia,
[("outputnode.corrected_pet_to_t1", "in_file")])])
workflow.connect([(petquant, scale_PVC_matrix,
[("outputnode.pet_results_npz", "in_file")])])
'''
Connect outputnode
'''
workflow.connect([(t1_to_dwi, outputnode, [("out_file", "t1_to_dwi")])])
workflow.connect([(dtiproc, outputnode, [
("outputnode.t1", "t1"),
("outputnode.wm_prob", "wm_prob"),
("outputnode.gm_prob", "gm_prob"),
("outputnode.csf_prob", "csf_prob"),
("outputnode.single_fiber_mask", "single_fiber_mask"),
("outputnode.fa", "fa"),
("outputnode.rgb_fa", "rgb_fa"),
("outputnode.md", "md"),
("outputnode.mode", "mode"),
("outputnode.t1_brain", "t1_brain"),
("outputnode.wm_mask", "wm_mask"),
("outputnode.term_mask", "term_mask"),
("outputnode.aparc_aseg", "aparc_aseg"),
("outputnode.tissue_class_files", "tissue_class_files"),
])])
workflow.connect([(reg_label, outputnode, [
("outputnode.rois_to_dwi", "rois_to_dwi"),
("outputnode.wmmask_to_dwi", "wmmask_to_dwi"),
("outputnode.termmask_to_dwi", "termmask_to_dwi"),
("outputnode.dwi_to_t1_matrix", "dwi_to_t1_matrix"),
("outputnode.highres_t1_to_dwi_matrix", "highres_t1_to_dwi_matrix"),
])])
if manual_seg_rois:
workflow.connect([(inputnode, outputnode, [("manual_seg_rois", "rois")
])])
else:
workflow.connect([(reg_label, outputnode, [("outputnode.rois", "rois")
])])
workflow.connect([(compute_AIF_PET, outputnode,
[("out_file", "SUV_corrected_pet_to_t1")])])
workflow.connect([(compute_SUV_norm_glycemia, outputnode,
[("out_file", "AIF_corrected_pet_to_t1")])])
workflow.connect([(petquant, outputnode, [("outputnode.orig_pet_to_t1",
"orig_pet_to_t1")])])
workflow.connect([(scale_PVC_matrix, outputnode, [("out_npz",
"pet_results_npz")])])
workflow.connect([(scale_PVC_matrix, outputnode, [("out_matlab_mat",
"pet_results_mat")])])
workflow.connect([(single_fiber_mask_cortex_only, outputnode,
[("out_file", "single_fiber_mask_cortex_only")])])
return workflow
outfields=info.keys()),
name='datasource')
datasource.inputs.template = "%s/%s"
datasource.inputs.base_directory = data_path
datasource.inputs.field_template = dict(pet_image='data/%s/%s_%s.nii.gz',
roi_image='data/%s/%s.nii.gz',
t1_image='data/%s/%s.nii.gz')
datasource.inputs.template_args = info
datasource.inputs.sort_filelist = True
datasink = pe.Node(interface=nio.DataSink(), name="datasink")
datasink.inputs.base_directory = output_dir
datasink.overwrite = True
petquant = create_pet_quantification_wf("example_petquant")
workflow = pe.Workflow(name='ex_petquant')
workflow.base_dir = output_dir
workflow.connect([(infosource, datasource, [('subject_id', 'subject_id')])])
workflow.connect(
[(infosource, petquant, [('subject_id', 'inputnode.subject_id')])])
workflow.connect([(datasource, petquant, [('pet_image', 'inputnode.pet')])])
workflow.connect([(datasource, petquant, [('roi_image', 'inputnode.rois')])])
workflow.connect([(datasource, petquant, [('t1_image', 'inputnode.t1')])])
workflow.connect(
[(petquant, datasink, [("outputnode.out_files", "@subject_id.out_files"),
("outputnode.corrected_pet_to_t1",
"@subject_id.corrected_pet_to_t1"),
